In [1]:
# Import libraries
import os
import pathlib
import hvplot.pandas
import hvplot.xarray
import holoviews as hv
import geopandas as gpd
import rioxarray as rxr
import xarray as xr
import pandas as pd
from pathlib import Path
from glob import glob
import earthpy
import earthpy.api.appeears as eaapp
In [2]:
# Make project folder
project = earthpy.Project(
'Gila River Vegetation', dirname='Gila_Vegetation_Data')
project.get_data()
# Load in the boundary data
aitsn_gdf = gpd.read_file(project.project_dir / 'tl_2020_us_aitsn')
In [3]:
# Select and merge the subdivisions you want
prir_gdf = aitsn_gdf[aitsn_gdf["AIANNHCE"]== '2810']
prir_gdf #for Ogala Lakota Nation
# Plot the results with web tile images
prir_gdf.plot()
Out[3]:
<Axes: >
In [4]:
# Select and merge the subdivisions you want
prir_gdf = prir_gdf.loc[prir_gdf.AIANNHCE=='2810'].dissolve()
# Plot the results with web tile images
prir_gdf.hvplot(
geo=True, tiles='EsriImagery',
fill_color=None, line_color='black',
title="Pine Ridge Indian Community",
frame_width=500)
Out[4]:
In [5]:
# Initialize AppeearsDownloader for MODIS NDVI data
ndvi_downloader = eaapp.AppeearsDownloader(
download_key='pric-ndvi',
project=project,
product='MOD13Q1.061',
layer='_250m_16_days_NDVI',
start_date="06-01",
end_date="09-01",
recurring=True,
year_range=[2016, 2022],
polygon=prir_gdf
)
In [6]:
# Download the prepared download -- this can take some time!
ndvi_downloader.download_files(cache=True)
Out[6]:
<generator object Path.rglob at 0x74591c2b3670>
In [7]:
# Get a list of NDVI tif file paths
project.project_dir = '/workspaces/data/gila_vegetation_data/pric-ndvi'
project.project_dir
ndvi_paths = sorted(glob(os.path.join(project.project_dir, '**', '*NDVI*.tif')))
ndvi_paths
Out[7]:
['/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2016145000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2016161000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2016177000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2016193000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2016209000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2016225000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2016241000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2017145000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2017161000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2017177000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2017193000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2017209000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2017225000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2017241000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2018145000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2018161000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2018177000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2018193000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2018209000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2018225000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2018241000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2019145000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2019161000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2019177000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2019193000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2019209000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2019225000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2019241000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2020145000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2020161000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2020177000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2020193000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2020209000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2020225000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2020241000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2021145000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2021161000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2021177000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2021193000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2021209000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2021225000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2021241000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2022145000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2022161000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2022177000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2022193000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2022209000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2022225000000_aid0001.tif', '/workspaces/data/gila_vegetation_data/pric-ndvi/MOD13Q1.061_2016138_to_2022244/MOD13Q1.061__250m_16_days_NDVI_doy2022241000000_aid0001.tif']
In [8]:
doy_start = -25
doy_end = -18
# Loop through each NDVI image
ndvi_das = []
for ndvi_path in ndvi_paths:
ndvi_path = Path(ndvi_path)
# Get date from file name
doy = ndvi_path.name[doy_start:doy_end]
date = pd.to_datetime(doy, format='%Y%j')
# Open dataset
da = rxr.open_rasterio(ndvi_path, mask_and_scale=True).squeeze()
# Add date dimension and clean up metadata
da = da.assign_coords({'date': date})
da = da.expand_dims({'date': 1})
da.name = 'NDVI'
# Prepare for concatenation
ndvi_das.append(da)
ndvi_das
Out[8]:
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In [9]:
# Combine NDVI images from all dates
da = xr.combine_by_coords(ndvi_das, coords=['date'])
/tmp/ipykernel_2071/2757418632.py:2: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly. da = xr.combine_by_coords(ndvi_das, coords=['date']) /tmp/ipykernel_2071/2757418632.py:2: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly. da = xr.combine_by_coords(ndvi_das, coords=['date'])
In [35]:
#calculate difference in NDVI
ndvi_diff = (
da.sel(date=slice('2018')).mean('date').NDVI
- da.sel(date=slice('2016')).mean('date').NDVI
)
#Plot the difference in NDVI
combined_plot1 =(ndvi_diff.hvplot(x='x', y='y', cmap='PiYG',
geo=True,
title='Change in NDVI @ Pine Ridge Indian Community\n2018 vs. 2016')
*
prir_gdf.hvplot(geo=True, fill_color=None, line_color='black'))
combined_plot1
Out[35]:
In [36]:
#calculate difference in NDVI
ndvi_diff2 = (
da.sel(date=slice('2022')).mean('date').NDVI
- da.sel(date=slice('2019')).mean('date').NDVI
)
#Plot the difference in NDVI
combined_plot2 =(ndvi_diff2.hvplot(x='x', y='y', cmap='PiYG',
geo=True,
title='Change in NDVI @ Pine Ridge Indian Community\n2022 vs. 2019')
*
prir_gdf.hvplot(geo=True, fill_color=None, line_color='black'))
combined_plot2
Out[36]:
In [ ]:
# Compute mean annual July NDVI for 2016-2018
ndvi_july_1618 = (
da.sel(date=slice('2016','2018'))
.where(da.date.dt.month == 7, drop=True)
.groupby('date.year')
.mean('date')
.NDVI)
print(ndvi_july_1618.head())
# Compute mean annual July NDVI for 2019-2022
ndvi_july_1922 = (
da.sel(date=slice('2019','2022'))
.where(da.date.dt.month == 7, drop=True)
.groupby('date.year')
.mean('date')
.NDVI)
print(ndvi_july_1922.head())
<xarray.DataArray 'NDVI' (year: 4, y: 5, x: 5)> Size: 400B
array([[[0.3188 , 0.3272 , 0.3464 , 0.35399997, 0.35399997],
[0.3189 , 0.31755 , 0.31904998, 0.31904998, 0.33069998],
[0.31379998, 0.31835 , 0.31835 , 0.33195 , 0.3423 ],
[0.3226 , 0.3226 , 0.3252 , 0.3363 , 0.33269998],
[0.333 , 0.33185 , 0.3401 , 0.34595 , 0.34595 ]],
[[0.30275 , 0.30285 , 0.3083 , 0.30885 , 0.30885 ],
[0.30065 , 0.30525 , 0.3083 , 0.3083 , 0.30885 ],
[0.3172 , 0.3164 , 0.3164 , 0.31839997, 0.31695 ],
[0.3247 , 0.3247 , 0.31629997, 0.31375 , 0.32 ],
[0.3123 , 0.3213 , 0.31440002, 0.3171 , 0.3171 ]],
[[0.7323 , 0.7112 , 0.69145 , 0.60405004, 0.60405004],
[0.7336 , 0.72685003, 0.71844995, 0.71844995, 0.69275 ],
[0.7271 , 0.71615 , 0.71615 , 0.7053 , 0.7014 ],
[0.66565 , 0.66565 , 0.68595 , 0.67345 , 0.70005 ],
[0.64695 , 0.66279995, 0.65489995, 0.65744996, 0.65744996]],
[[0.70585 , 0.7014 , 0.70645 , 0.67149997, 0.67149997],
[0.7084 , 0.70079994, 0.70419997, 0.70419997, 0.69365 ],
[0.69200003, 0.70079994, 0.70079994, 0.68964994, 0.70489997],
[0.70554996, 0.70554996, 0.70554996, 0.70869994, 0.69914997],
[0.66335 , 0.6727 , 0.67695 , 0.68104994, 0.68104994]]],
dtype=float32)
Coordinates:
* x (x) float64 40B -103.0 -103.0 -103.0 -103.0 -103.0
* y (y) float64 40B 43.8 43.79 43.79 43.79 43.79
band int64 8B 1
spatial_ref int64 8B 0
* year (year) int64 32B 2016 2017 2018 2019
Attributes:
units: NDVI
AREA_OR_POINT: Area
<xarray.DataArray 'NDVI' (year: 4, y: 5, x: 5)> Size: 400B
array([[[0.70585 , 0.7014 , 0.70645 , 0.67149997, 0.67149997],
[0.7084 , 0.70079994, 0.70419997, 0.70419997, 0.69365 ],
[0.69200003, 0.70079994, 0.70079994, 0.68964994, 0.70489997],
[0.70554996, 0.70554996, 0.70554996, 0.70869994, 0.69914997],
[0.66335 , 0.6727 , 0.67695 , 0.68104994, 0.68104994]],
[[0.35635 , 0.351 , 0.34995 , 0.3505 , 0.3505 ],
[0.3467 , 0.3467 , 0.33765 , 0.33765 , 0.33375 ],
[0.36325 , 0.35219997, 0.35219997, 0.3366 , 0.35525 ],
[0.3645 , 0.3645 , 0.3466 , 0.3486 , 0.3886 ],
[0.3777 , 0.37734997, 0.37589997, 0.37510002, 0.37510002]],
[[0.30955 , 0.30475 , 0.28785 , 0.27225 , 0.27225 ],
[0.30335 , 0.30475 , 0.2852 , 0.2852 , 0.27749997],
[0.3087 , 0.30159998, 0.30159998, 0.29764998, 0.29145 ],
[0.3247 , 0.3247 , 0.3226 , 0.31075 , 0.29685 ],
[0.32755 , 0.3245 , 0.32549998, 0.33139998, 0.33139998]],
[[0.28394997, 0.2805 , 0.27104998, 0.26424998, 0.26424998],
[0.2881 , 0.2913 , 0.27365 , 0.27365 , 0.2644 ],
[0.3006 , 0.30035 , 0.30035 , 0.27365 , 0.28245 ],
[0.3094 , 0.3094 , 0.31794998, 0.29904997, 0.3017 ],
[0.31855 , 0.3186 , 0.31849998, 0.31054997, 0.31054997]]],
dtype=float32)
Coordinates:
* x (x) float64 40B -103.0 -103.0 -103.0 -103.0 -103.0
* y (y) float64 40B 43.8 43.79 43.79 43.79 43.79
band int64 8B 1
spatial_ref int64 8B 0
* year (year) int64 32B 2019 2020 2021 2022
Attributes:
units: NDVI
AREA_OR_POINT: Area
In [48]:
mean_1618 = ndvi_july_1618.mean('year')
mean_1922 = ndvi_july_1922.mean('year')
july_diff = mean_1922 - mean_1618
july_diff.hvplot(
x='x',
y='y',
cmap='PiYG',
clim=(-0.3, 0.3),
geo=True,
title='Change in July NDVI: 2019–2022 minus 2016–2018'
)
Out[48]:
In [44]:
# Plot means between the two time periods
# Compute spatial mean and drop extra columns
ndvi_july_1618_mean = (ndvi_july_1618
.mean(['x','y']).to_dataframe()
.drop(columns=['band','spatial_ref'], errors='ignore'))
ndvi_july_1922_mean = (ndvi_july_1922
.mean(['x','y']).to_dataframe()
.drop(columns=['band','spatial_ref'], errors='ignore'))
# Join datasets
july_ndvi_combined = (
ndvi_july_1618_mean
.rename(columns={'NDVI':'NDVI 2016-2018'})
.join(ndvi_july_1922_mean
.rename(columns={'NDVI':'NDVI 2019-2022'}),
how='outer')
.reset_index())
# Plot as lines
july_ndvi_combined.hvplot(
x='year',
y=['NDVI 2016-2018','NDVI 2019-2022'],
title='Mean July NDVI 2016-18 vs 2019-22 in PRIC',
ylabel='Mean July NDVI')
Out[44]: